smollm2-1.7b-aptitude-qa-v1/README.md

license, base_model, tags, datasets, language, pipeline_tag

license	base_model	tags	datasets	language	pipeline_tag
apache-2.0	HuggingFaceTB/SmolLM2-1.7B-Instruct	text-generation-inference transformers lora trl smollm2 aptitude	Prathamesh25/aptitude-qa-dataset	en	text-generation

SmolLM2 1.7B Aptitude QA

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-1.7B-Instruct optimized for technical aptitude question-answering tasks, including basic programming concepts (C, Python, loops) and fundamental AI/ML reasoning logic.

It was trained using LoRA parameters in pure Float16 precision on a free Google Colab T4 GPU node to handle the target dataset schema formatting seamlessly.

Model Description

• Developed by: Prathamesh25
• Base Model: HuggingFaceTB/SmolLM2-1.7B-Instruct
• Dataset Used: Prathamesh25/aptitude-qa-dataset
• Language: English
• License: Apache 2.0

Training Information & Hyperparameters

• Precision: Pure Float16 (fp16=True)
• Optimizer: adamw_torch
• Learning Rate: 2e-4
• Per Device Train Batch Size: 4
• Gradient Accumulation Steps: 4
• LoRA Configuration: Rank (r)=16, Alpha (lpha)=32, Dropout=0.05
• Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Loss Progression Metrics

The training process showed highly stable optimization across epochs:

• Step 10: 1.955
• Step 30: 0.709
• Step 100: 0.412
• Step 200: 0.323
• Step 330: 0.281

How to Use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Prathamesh25/smollm2-1.7b-aptitude-qa-v1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    device_map="auto"
)

test_prompt = [
    {
        "role": "user", 
        "content": "subject: 06 technical aptitude (basic programming and aiml concepts)\nlevel: 01 basic\ncategory: loops\nquestion: what is the output of this c code: `int sum = 0; for(int i=1; i<=3; i++) { sum += i; } printf(\"%d\", sum);`?"
    }
]

text_prompt = tokenizer.apply_chat_template(test_prompt, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text_prompt], return_tensors="pt").to("cuda")
input_token_len = model_inputs.input_ids.shape[1]

with torch.no_grad():
    generated_ids = model.generate(
        input_ids=model_inputs.input_ids,
        attention_mask=model_inputs.attention_mask,
        max_new_tokens=150, 
        temperature=0.1, 
        do_sample=True,        
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.eos_token_id
    )

final_tokens = generated_ids[0][input_token_len:]
print(tokenizer.decode(final_tokens, skip_special_tokens=True))

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